Yearly Traffic Safety Analysis

381 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Hamilton County, total traffic crashes increased by 6.1% from 359 in 2024 to 381 in 2025. This rise was accompanied by a more significant increase in crash severity. The most notable year-over-year shift was the increase in traffic fatalities from 3 to 5 and a more than doubling of serious injury crashes from 4 to 11.

381

▲ 6.1%was 359

Total Crash Events

5

▲ 66.7%was 3

Persons Killed

113

▲ 34.5%was 84

Persons Injured

5

▲ 66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Hamilton County worsened year-over-year. Total crashes rose from 359 to 381, an increase of 6.1%. The number of persons injured increased by 34.5% from 84 to 113, and the number of fatalities increased from 3 to 5.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 3↑66.7%

2

Pedestrians Injured

Prior: 0→%

111

Motorists Injured

Prior: 82↑35.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Thursday (65 crashes) in the prior period to Friday (67 crashes) in the current period. A similar change occurred with the peak hour, which moved from the 4 p.m. hour in 2024 (30 crashes) to the 5 p.m. hour in 2025 (34 crashes). The overall pattern of crashes remains concentrated in the late afternoon.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes increased compared to the prior year. Fatal crashes rose from 3 to 5, increasing their share of all crashes from 0.8% to 1.3%. The number of crashes resulting in serious injuries more than doubled, increasing from 4 incidents (1.1% of total) to 11 incidents (2.9% of total). Consequently, the proportion of non-injury crashes decreased slightly from 77.7% to 76.6% of all events.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.3%
↑66.7%prior 3
Serious Injury11serious injury crashes2.9%
↑175.0%prior 4
Minor Injury31minor injury crashes8.1%
↑3.3%prior 30
Possible Injury42possible injury crashes11%
↓-2.3%prior 43
No Injury292no injury crashes76.6%
↑4.7%prior 279

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the most common contributing factor in both periods, though the count decreased from 81 to 79. The most significant change was a 143.8% increase in the count of crashes attributed to "Driving too fast for conditions," which rose from 16 to 39 incidents, becoming the second-leading factor. Crashes where a vehicle "Ran off road - straight" also increased in count by 60.9%, from 23 to 37 incidents.

Officer-Reported Primary Contributing Cause

Animal79 (20.7%)↓-2.5%prior 81
Driving too fast for conditions39 (10.2%)↑143.8%prior 16
Ran off road - straight37 (9.7%)↑60.9%prior 23
Other (explain in narrative): Other33 (8.7%)↓-10.8%prior 37
Lost Control32 (8.4%)↑6.7%prior 30
FTYROW: From stop sign21 (5.5%)↑31.3%prior 16
Ran off road - left17 (4.5%)↓-5.6%prior 18
Driver Distraction: Other interior distraction12 (3.1%)↑33.3%prior 9
Followed too close10 (2.6%)→0.0%prior 10
Ran Stop Sign8 (2.1%)↑60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While clear weather and dry road conditions accounted for the majority of crashes in both years, incidents in adverse conditions saw a notable increase. Crashes occurring in snow or blowing snow increased from a combined 26 incidents to 54. Similarly, collisions on roads with snow or ice on the surface rose from 58 to 78. Crashes in darkness on unlit roadways also increased from 67 to 87 incidents year-over-year.

Weather

Clear188 (57.5%)
↓-5.5%prior 199
Cloudy54 (16.5%)
↑17.4%prior 46
Snow28 (8.6%)
↑55.6%prior 18
Blowing Snow26 (8.0%)
↑225.0%prior 8
Rain19 (5.8%)
↑90.0%prior 10
Severe Winds5 (1.5%)
Sleet, hail3 (0.9%)
Freezing rain/drizzle2 (0.6%)
↓-71.4%prior 7
Fog, smoke, smog2 (0.6%)
↓-75.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight208 (62.8%)
↑10.6%prior 188
Dark - roadway not lighted87 (26.3%)
↑29.9%prior 67
Dark - roadway lighted23 (6.9%)
↓-17.9%prior 28
Dawn7 (2.1%)
↓-30.0%prior 10
Dusk5 (1.5%)
↓-37.5%prior 8
Dark - unknown roadway lighting1 (0.3%)
↓-80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry201 (61.3%)
↓-4.7%prior 211
Snow39 (11.9%)
↑62.5%prior 24
Ice/frost39 (11.9%)
↑14.7%prior 34
Wet35 (10.7%)
↑40.0%prior 25
Slush8 (2.4%)
Gravel6 (1.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles most frequently involved in crashes remained consistent, with Chevrolet and Ford models representing the highest volumes in both periods. Regarding the age of persons involved, there was a significant increase in the 65+ age group, which grew from 58 individuals in the prior period to 89 in the current period. The number of persons aged 16-20 involved in crashes also increased from 56 to 71.

Top Vehicle Makes (582 vehicles)

1
CHEV93 (16%)
↑27.4%prior 73
2
FORD87 (14.9%)
↑40.3%prior 62
3
CHEVROLET28 (4.8%)
↓-31.7%prior 41
4
GMC26 (4.5%)
↓-10.3%prior 29
5
TOYT24 (4.1%)
→0.0%prior 24
6
FREIGHTLINER23 (4%)
↑35.3%prior 17
7
JEEP22 (3.8%)
↑29.4%prior 17
8
DODG18 (3.1%)
↓-10.0%prior 20
9
KIA15 (2.6%)
↑200.0%prior 5
10
BUIC15 (2.6%)
↑36.4%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

61 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (370 persons with recorded sex)

Male251 (67.8%)
↑15.7%prior 217
Female119 (32.2%)
↑10.2%prior 108

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2025-01-01 through 2025-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 381
  • Total persons involved: 612
  • Total vehicles involved: 582

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2025." Published September 9, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2025-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

ThatCarHitMe.com · An Injuria.ai Company